In addition we visualise pressure mat data to explore the potential of the sensor to capture exercise performance quality.
However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views.
In order to recognize human activities, we propose a human body parts tracking system that tracks human body parts such as head, torso, arms and legs in order to perform activity recognition tasks in real time.
Activity recognition is the ability to identify and recognize the action or goals of the agent.
We further divide these areas into ten different sub-topics and present the latest research work in these sub-topics.
While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to significantly reduce the size of the training set required to initialize the recognition model.
Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people.
The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR).
Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR).